"""
Frox AI Morph 1.1 — KV Cache Tests
Run with: pytest tests/test_kv_cache.py -v

Includes a dedicated regression test for a real bug caught during
development: `MorphSessionCache.chat()`-style usage originally called
`cache.step(0)` after writing new tokens, which is a no-op — `_seq_len`
never advanced, so every write silently landed back at offset 0 and
overwrote whatever was written before it. `test_sequential_updates_append_not_overwrite`
below fails immediately on that bug and passes with the fix.
"""
from __future__ import annotations

import pytest
import torch

from inference.cache.kv_cache import MorphKVCache, MorphSessionCache


NUM_LAYERS = 2
NUM_KV_HEADS = 2
HEAD_DIM = 8
BLOCK_SIZE = 4   # small on purpose to force multiple blocks quickly


@pytest.fixture
def cache() -> MorphKVCache:
    return MorphKVCache(
        num_layers=NUM_LAYERS,
        num_kv_heads=NUM_KV_HEADS,
        head_dim=HEAD_DIM,
        block_size=BLOCK_SIZE,
        dtype=torch.float32,
        device=torch.device("cpu"),
    )


def _rand_kv(seq_len: int):
    k = torch.randn(1, NUM_KV_HEADS, seq_len, HEAD_DIM)
    v = torch.randn(1, NUM_KV_HEADS, seq_len, HEAD_DIM)
    return k, v


class TestBasicReadWrite:
    def test_single_write_round_trips(self, cache):
        k, v = _rand_kv(3)
        cache.update(0, k, v)
        cache.step(3)

        k_read, v_read = cache.get(0)
        assert torch.allclose(k_read, k)
        assert torch.allclose(v_read, v)
        assert cache.seq_len == 3

    def test_empty_cache_returns_zero_length(self, cache):
        k, v = cache.get(0)
        assert k.shape[2] == 0
        assert v.shape[2] == 0
        assert cache.seq_len == 0


class TestSequentialAppend:
    """
    The core regression test: writing in several separate calls (as
    happens once per generated token in `MorphInferenceEngine.chat()`)
    must APPEND, never overwrite. This is exactly the bug that was
    caught: calling `step(0)` instead of `step(delta_len)` made every
    write land at offset 0 again.
    """

    def test_sequential_updates_append_not_overwrite(self, cache):
        chunks = [_rand_kv(1) for _ in range(6)]   # simulate 6 generated tokens, one at a time

        for k, v in chunks:
            cache.update(0, k, v)
            cache.step(1)   # <- the fix: must advance by the actual delta length

        assert cache.seq_len == 6

        k_read, v_read = cache.get(0)
        assert k_read.shape[2] == 6, (
            f"Expected 6 cached tokens after 6 sequential single-token writes, "
            f"got {k_read.shape[2]} — writes are overwriting instead of appending "
            f"(this is exactly the step(0) vs step(delta_len) bug)."
        )

        expected_k = torch.cat([c[0] for c in chunks], dim=2)
        expected_v = torch.cat([c[1] for c in chunks], dim=2)
        assert torch.allclose(k_read, expected_k), "Cached content doesn't match write order"
        assert torch.allclose(v_read, expected_v)

    def test_mixed_batch_and_single_writes(self, cache):
        """A multi-token prefill followed by several single-token decode steps."""
        prefill_k, prefill_v = _rand_kv(5)
        cache.update(0, prefill_k, prefill_v)
        cache.step(5)

        decode_chunks = [_rand_kv(1) for _ in range(4)]
        for k, v in decode_chunks:
            cache.update(0, k, v)
            cache.step(1)

        assert cache.seq_len == 9
        k_read, _ = cache.get(0)
        assert k_read.shape[2] == 9

        expected = torch.cat([prefill_k] + [c[0] for c in decode_chunks], dim=2)
        assert torch.allclose(k_read, expected)

    def test_all_layers_stay_in_sync(self, cache):
        """
        step() is called once per forward pass (not once per layer), so
        every layer must end up with the same seq_len even though
        update() is called once per layer per step.
        """
        for _ in range(5):
            for layer_idx in range(NUM_LAYERS):
                k, v = _rand_kv(1)
                cache.update(layer_idx, k, v)
            cache.step(1)   # called once, after all layers for this step

        for layer_idx in range(NUM_LAYERS):
            k, v = cache.get(layer_idx)
            assert k.shape[2] == 5, f"Layer {layer_idx} has {k.shape[2]} tokens, expected 5"


class TestPagedAllocation:
    def test_grows_across_block_boundary(self, cache):
        """block_size=4 in the fixture; write more than one block's worth."""
        k, v = _rand_kv(10)   # spans 3 blocks (4+4+2)
        cache.update(0, k, v)
        cache.step(10)

        k_read, v_read = cache.get(0)
        assert k_read.shape[2] == 10
        assert torch.allclose(k_read, k)

    def test_memory_scales_with_allocated_blocks_not_max_seq_len(self, cache):
        """A short sequence shouldn't allocate memory for a hypothetical long one."""
        k, v = _rand_kv(1)
        cache.update(0, k, v)
        cache.step(1)

        # Only layer 0 was written; layer 1 should have zero blocks allocated
        mem = cache.memory_mb()
        assert mem > 0
        # Rough sanity: memory should be proportional to ~1 block for 1 layer,
        # not proportional to some large fixed max_seq_len.
        bytes_per_block = NUM_KV_HEADS * BLOCK_SIZE * HEAD_DIM * 4 * 2  # k+v, float32
        expected_mb = bytes_per_block / (1024 ** 2)
        assert mem <= expected_mb * 1.5  # generous tolerance


class TestUtilization:
    def test_utilization_reflects_fill_ratio(self, cache):
        k, v = _rand_kv(2)   # half of one 4-token block
        cache.update(0, k, v)
        cache.step(2)
        assert cache.utilization() == pytest.approx(0.5, abs=0.01)


class TestReset:
    def test_reset_clears_everything(self, cache):
        k, v = _rand_kv(5)
        cache.update(0, k, v)
        cache.step(5)
        assert cache.seq_len == 5

        cache.reset()
        assert cache.seq_len == 0
        k_read, _ = cache.get(0)
        assert k_read.shape[2] == 0


class TestSessionCache:
    def test_sessions_are_isolated(self):
        sessions = MorphSessionCache(
            num_layers=NUM_LAYERS, num_kv_heads=NUM_KV_HEADS, head_dim=HEAD_DIM,
            block_size=BLOCK_SIZE, device=torch.device("cpu"), max_sessions=4,
        )
        cache_a = sessions.get_or_create("session-a")
        cache_b = sessions.get_or_create("session-b")

        k, v = _rand_kv(3)
        cache_a.update(0, k, v)
        cache_a.step(3)

        assert cache_a.seq_len == 3
        assert cache_b.seq_len == 0, "Writing to session A must not affect session B"

    def test_lru_eviction(self):
        sessions = MorphSessionCache(
            num_layers=NUM_LAYERS, num_kv_heads=NUM_KV_HEADS, head_dim=HEAD_DIM,
            block_size=BLOCK_SIZE, device=torch.device("cpu"), max_sessions=2,
        )
        sessions.get_or_create("a")
        sessions.get_or_create("b")
        sessions.get_or_create("a")   # touch "a" again — "b" is now the LRU
        sessions.get_or_create("c")   # should evict "b", not "a"

        assert "a" in sessions._caches
        assert "b" not in sessions._caches
        assert "c" in sessions._caches

    def test_reset_session_preserves_other_sessions(self):
        sessions = MorphSessionCache(
            num_layers=NUM_LAYERS, num_kv_heads=NUM_KV_HEADS, head_dim=HEAD_DIM,
            block_size=BLOCK_SIZE, device=torch.device("cpu"),
        )
        cache_a = sessions.get_or_create("a")
        cache_b = sessions.get_or_create("b")
        for c in (cache_a, cache_b):
            k, v = _rand_kv(3)
            c.update(0, k, v)
            c.step(3)

        sessions.reset_session("a")
        assert sessions.get_or_create("a").seq_len == 0
        assert sessions.get_or_create("b").seq_len == 3